for (i in 1:length(params))
print(paste('Parameter:', names(params)[i], ' - Value:', params[[i]], '- Class:', class(params[[i]])))
## [1] "Parameter: Dataset - Value: CHD2_iPSCs_and_organoids_PublicRepo - Class: character"
## [1] "Parameter: SEFile - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/day25/Output/Savings/day25CbO.SE_deseq2_HT.rds - Class: character"
## [1] "Parameter: DEAList - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/day25/Output/Savings/day25CbO.DEAList_HT.rds - Class: character"
## [1] "Parameter: HT - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/day25/Output/Savings/day25CbO.deseqHTvsWT.rds - Class: character"
## [1] "Parameter: SavingFolder - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/6.Enrichments/day25/Output/Savings/ - Class: character"
## [1] "Parameter: FiguresFolder - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/6.Enrichments/day25/Output/Figures/ - Class: character"
## [1] "Parameter: FDRthr - Value: 0.05 - Class: numeric"
## [1] "Parameter: logFCthr - Value: 0.55 - Class: numeric"
## [1] "Parameter: TopGO - Value: BP_MF_CC - Class: character"
## [1] "Parameter: GoEnTh - Value: 1 - Class: numeric"
## [1] "Parameter: GoPvalTh - Value: 0.05 - Class: numeric"
## [1] "Parameter: NbName - Value: TopGO_day25_HT - Class: character"
## [1] "Parameter: SaveImages - Value: FALSE - Class: logical"library(RNASeqBulkExploratory)
library(SummarizedExperiment)
library(tidyr)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(topGO)
library(sechm)
library(ggplot2)
library(grid)
library(gridExtra)
library(RColorBrewer)
library(cowplot)
library(data.table)
source('/group/testa/Users/oliviero.leonardi/myProjects/CHD2/BulkRNAseq/ContainerHome/CHD2_organoids/NoGradientBarplots.R')Dataset <- params$Dataset
logFCthr <- params$logFCthr
FDRthr <- params$FDRthr
FdrTh <- FDRthr
logFcTh <- logFCthr
SavingFolder <- ifelse(is.null(params$SavingFolder), getwd(), params$SavingFolder)
FiguresFolder <- ifelse(is.null(params$FiguresFolder), getwd(), params$FiguresFolder)
if (dir.exists(SavingFolder) == FALSE) {
dir.create(SavingFolder, recursive=TRUE)
}#SE object coming from DEA, but not containing specific contrast results
SE_DEA <- readRDS(params$SEFile)
SE_DEA <- SE_DEA[rowData(SE_DEA)$GeneName != '', ]
rownames(SE_DEA) <- rowData(SE_DEA)$GeneName
# List with differential expression results (all time-points)
DEA <- readRDS(params$DEAList)colvector <- c("#5ec962", "#e95462", "#2c728e")
names(colvector) <- c('All', 'Up', 'Down')if(! identical(rownames(SE_DEA), row.names(DEA$HT$res))){
stop('Expression data in SE and results from differential espression analysis are inconsistent.')
}
SE_DEA <- mergeDeaSE(SE_DEA, DEA$HT$res, subsetRowDataCols=NULL,
logFcCol='log2FoldChange', FdrCol='padj') #specify
## Renaming " log2FoldChange " to "logFC"
## Renaming " padj " to "FDR"17781 genes in 13 samples have been testes for differential expression.
The following number of genes are identified as differentially expressed:
Imposing a threshold of 0.55 on the Log2FC and 0.05 on the FDR (as specified in parameters), 2653 genes are selected: 2300 up-regulated genes and 1931 down-regulated genes.
The results of the differential expression analysis are visualized by Volcano plot. An interactive version is included in the html (only genes with FDR < threshold), while a static version is saved.
plotVolcanoSE(SE=SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr,
FdrCeil=1e-10, logFcCeil=4, Interactive = FALSE)## Warning: Removed 1035 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 17584 rows containing missing values or values outside the scale range
## (`geom_text_repel()`).
## Warning: ggrepel: 128 unlabeled data points (too many overlaps). Consider increasing max.overlaps
plotVolcanoSE(SE=SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr,
FdrCeil=1e-10, logFcCeil=4, Interactive = TRUE)Heatmaps for DEGs, showing scaled vst values.
DEGs <- dplyr::filter(data.frame(rowData(SE_DEA)), FDR < FDRthr & abs(logFC) > log2(logFCthr))
ScaledCols <- c('darkblue', "purple","white","lightgoldenrod1", 'goldenrod1')# sechm::sechm(SE_DEA, genes=DEGs$GeneName, assayName="vst", gaps_at="Genotype", show_rownames=FALSE,
# top_annotation=c('Genotype'), hmcols=ScaledCols, show_colnames=TRUE,
# do.scale=TRUE, breaks=0.85, column_title = "Scaled Vst Values")Gene ontology enrichment analysis is performed on the set of 2653 genes using TopGO with Fisher statistics and weight01 algorithm.
For each specified domain of the ontology:
I generate vectors for the gene universe, all modulated genes, up-regulated genes and down-regulated genes in the format required by TopGo.
GeneVectors <- topGOGeneVectors(SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr)## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1
Therefore:
Then I set parameters according to the gene ontology domains to be evaluated. By default, Biological Process and Molecular Function domains are interrogated.
BpEval <- ifelse(length(grep('BP', params$TopGO))!=0, TRUE, FALSE)
MfEval <- ifelse(length(grep('MF', params$TopGO))!=0, TRUE, FALSE)
CcEval <- ifelse(length(grep('CC', params$TopGO))!=0, TRUE, FALSE)On the basis of the analysis settings, the enrichment for Biological Process IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
BPannHT <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$DEGenes),
mapping="org.Hs.eg.db", ID="symbol") %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResBPAllHT <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=BPannHT, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPAllHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11815 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15469 GO terms and 35188 relations. )
##
## Annotating nodes ...............
## ( 14087 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 6783 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 3 nodes to be scored (0 eliminated genes)
##
## Level 18: 6 nodes to be scored (0 eliminated genes)
##
## Level 17: 12 nodes to be scored (31 eliminated genes)
##
## Level 16: 20 nodes to be scored (49 eliminated genes)
##
## Level 15: 58 nodes to be scored (139 eliminated genes)
##
## Level 14: 108 nodes to be scored (275 eliminated genes)
##
## Level 13: 186 nodes to be scored (671 eliminated genes)
##
## Level 12: 316 nodes to be scored (1547 eliminated genes)
##
## Level 11: 571 nodes to be scored (3743 eliminated genes)
##
## Level 10: 855 nodes to be scored (5869 eliminated genes)
##
## Level 9: 1025 nodes to be scored (7579 eliminated genes)
##
## Level 8: 1013 nodes to be scored (9576 eliminated genes)
##
## Level 7: 967 nodes to be scored (11149 eliminated genes)
##
## Level 6: 781 nodes to be scored (12294 eliminated genes)
##
## Level 5: 478 nodes to be scored (13077 eliminated genes)
##
## Level 4: 252 nodes to be scored (13602 eliminated genes)
##
## Level 3: 109 nodes to be scored (13781 eliminated genes)
##
## Level 2: 22 nodes to be scored (13875 eliminated genes)
##
## Level 1: 1 nodes to be scored (13924 eliminated genes)# Wrapper function for topGO analysis (see helper file)
ResBPDownHT <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=BPannHT, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPDownHT', outDir=paste0(SavingFolder))
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11815 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15469 GO terms and 35188 relations. )
##
## Annotating nodes ...............
## ( 14087 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4939 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 6 nodes to be scored (18 eliminated genes)
##
## Level 16: 11 nodes to be scored (25 eliminated genes)
##
## Level 15: 31 nodes to be scored (83 eliminated genes)
##
## Level 14: 55 nodes to be scored (184 eliminated genes)
##
## Level 13: 106 nodes to be scored (506 eliminated genes)
##
## Level 12: 194 nodes to be scored (1270 eliminated genes)
##
## Level 11: 321 nodes to be scored (3430 eliminated genes)
##
## Level 10: 559 nodes to be scored (5529 eliminated genes)
##
## Level 9: 748 nodes to be scored (7046 eliminated genes)
##
## Level 8: 757 nodes to be scored (9231 eliminated genes)
##
## Level 7: 751 nodes to be scored (10939 eliminated genes)
##
## Level 6: 634 nodes to be scored (12164 eliminated genes)
##
## Level 5: 423 nodes to be scored (12973 eliminated genes)
##
## Level 4: 216 nodes to be scored (13583 eliminated genes)
##
## Level 3: 102 nodes to be scored (13777 eliminated genes)
##
## Level 2: 21 nodes to be scored (13871 eliminated genes)
##
## Level 1: 1 nodes to be scored (13923 eliminated genes)
# Selection on enrichment of at least 2 is implemented (also to avoid depleted categories). Then categories are ranked by PVal and all the ones with Pval < th are selected. If the number is < minTerms, othter terms are included to reach the minimum number. GOTable(ResBPDownHT$ResSel, maxGO=20)ResBPUpHT <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=BPannHT, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPUpHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11815 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15469 GO terms and 35188 relations. )
##
## Annotating nodes ...............
## ( 14087 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 5932 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 3 nodes to be scored (0 eliminated genes)
##
## Level 18: 5 nodes to be scored (0 eliminated genes)
##
## Level 17: 10 nodes to be scored (31 eliminated genes)
##
## Level 16: 19 nodes to be scored (48 eliminated genes)
##
## Level 15: 53 nodes to be scored (132 eliminated genes)
##
## Level 14: 99 nodes to be scored (255 eliminated genes)
##
## Level 13: 161 nodes to be scored (642 eliminated genes)
##
## Level 12: 265 nodes to be scored (1497 eliminated genes)
##
## Level 11: 490 nodes to be scored (3575 eliminated genes)
##
## Level 10: 719 nodes to be scored (5536 eliminated genes)
##
## Level 9: 867 nodes to be scored (7264 eliminated genes)
##
## Level 8: 881 nodes to be scored (9243 eliminated genes)
##
## Level 7: 857 nodes to be scored (10970 eliminated genes)
##
## Level 6: 702 nodes to be scored (12211 eliminated genes)
##
## Level 5: 435 nodes to be scored (13009 eliminated genes)
##
## Level 4: 240 nodes to be scored (13593 eliminated genes)
##
## Level 3: 103 nodes to be scored (13779 eliminated genes)
##
## Level 2: 22 nodes to be scored (13874 eliminated genes)
##
## Level 1: 1 nodes to be scored (13923 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/BPUp'), recursive=TRUE)
#GOAnnotation(ResBPUp$ResSel, GOdata=ResBPUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/BPUp'), keytype='SYMBOL')GOTable(ResBPUpHT$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResBPAllHT$ResSel, TopGOResDown=ResBPDownHT$ResSel, TopGOResUp = ResBPUpHT$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResBPAllHT$ResSel, TopGOResDown = ResBPDownHT$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`On the basis of the analysis settings, the enrichment for Molecular Function IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
MFannHT <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResMFAllHT <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=MFannHT, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFAllHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4195 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4656 GO terms and 6011 relations. )
##
## Annotating nodes ...............
## ( 14512 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1226 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 5 nodes to be scored (0 eliminated genes)
##
## Level 11: 18 nodes to be scored (0 eliminated genes)
##
## Level 10: 25 nodes to be scored (34 eliminated genes)
##
## Level 9: 61 nodes to be scored (230 eliminated genes)
##
## Level 8: 111 nodes to be scored (1394 eliminated genes)
##
## Level 7: 194 nodes to be scored (3543 eliminated genes)
##
## Level 6: 245 nodes to be scored (4377 eliminated genes)
##
## Level 5: 279 nodes to be scored (6255 eliminated genes)
##
## Level 4: 216 nodes to be scored (9285 eliminated genes)
##
## Level 3: 57 nodes to be scored (11722 eliminated genes)
##
## Level 2: 14 nodes to be scored (12486 eliminated genes)
##
## Level 1: 1 nodes to be scored (14371 eliminated genes)ResMFDownHT <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFannHT, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFDownHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4195 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4656 GO terms and 6011 relations. )
##
## Annotating nodes ...............
## ( 14512 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 876 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 10 nodes to be scored (0 eliminated genes)
##
## Level 10: 20 nodes to be scored (21 eliminated genes)
##
## Level 9: 41 nodes to be scored (165 eliminated genes)
##
## Level 8: 71 nodes to be scored (1364 eliminated genes)
##
## Level 7: 132 nodes to be scored (3444 eliminated genes)
##
## Level 6: 169 nodes to be scored (4159 eliminated genes)
##
## Level 5: 203 nodes to be scored (5847 eliminated genes)
##
## Level 4: 164 nodes to be scored (8984 eliminated genes)
##
## Level 3: 50 nodes to be scored (11545 eliminated genes)
##
## Level 2: 13 nodes to be scored (12391 eliminated genes)
##
## Level 1: 1 nodes to be scored (14369 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/MFDown'), recursive=TRUE)
#GOAnnotation(ResMFDown$ResSel, GOdata=ResMFDown$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/MFDown'), keytype='SYMBOL')GOTable(ResMFDownHT$ResSel, maxGO=20)ResMFUpHT <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFannHT, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFUpHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4195 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4656 GO terms and 6011 relations. )
##
## Annotating nodes ...............
## ( 14512 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 993 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 4 nodes to be scored (0 eliminated genes)
##
## Level 11: 12 nodes to be scored (0 eliminated genes)
##
## Level 10: 18 nodes to be scored (28 eliminated genes)
##
## Level 9: 42 nodes to be scored (179 eliminated genes)
##
## Level 8: 85 nodes to be scored (1356 eliminated genes)
##
## Level 7: 144 nodes to be scored (3452 eliminated genes)
##
## Level 6: 192 nodes to be scored (4209 eliminated genes)
##
## Level 5: 235 nodes to be scored (5987 eliminated genes)
##
## Level 4: 190 nodes to be scored (8956 eliminated genes)
##
## Level 3: 56 nodes to be scored (11582 eliminated genes)
##
## Level 2: 14 nodes to be scored (12415 eliminated genes)
##
## Level 1: 1 nodes to be scored (14356 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/MFUp'), recursive=TRUE)
#GOAnnotation(ResMFUp$ResSel, GOdata=ResMFUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/MFUp'), keytype='SYMBOL')GOTable(ResMFUpHT$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResMFAllHT$ResSel, TopGOResDown=ResMFDownHT$ResSel, TopGOResUp=ResMFUpHT$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResMFAllHT$ResSel, TopGOResDown = ResMFDownHT$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
CCannHT <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResCCAllHT <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=CCannHT, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCAllHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1707 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3236 relations. )
##
## Annotating nodes ...............
## ( 14776 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 695 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 4 nodes to be scored (0 eliminated genes)
##
## Level 11: 31 nodes to be scored (47 eliminated genes)
##
## Level 10: 70 nodes to be scored (63 eliminated genes)
##
## Level 9: 100 nodes to be scored (817 eliminated genes)
##
## Level 8: 108 nodes to be scored (2672 eliminated genes)
##
## Level 7: 115 nodes to be scored (5077 eliminated genes)
##
## Level 6: 100 nodes to be scored (8882 eliminated genes)
##
## Level 5: 72 nodes to be scored (10679 eliminated genes)
##
## Level 4: 48 nodes to be scored (12804 eliminated genes)
##
## Level 3: 42 nodes to be scored (14127 eliminated genes)
##
## Level 2: 2 nodes to be scored (14555 eliminated genes)
##
## Level 1: 1 nodes to be scored (14702 eliminated genes)
#write.table(ResCCAll$ResAll, file=paste0(SavingFolder, 'TopGO/CCAllResults.txt'), sep='\t', row.names=FALSE)# Wrapper function for topGO analysis (see helper file)
ResCCDownHT <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=CCannHT, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCDownHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1707 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3236 relations. )
##
## Annotating nodes ...............
## ( 14776 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 560 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 23 nodes to be scored (47 eliminated genes)
##
## Level 10: 57 nodes to be scored (55 eliminated genes)
##
## Level 9: 75 nodes to be scored (730 eliminated genes)
##
## Level 8: 82 nodes to be scored (2560 eliminated genes)
##
## Level 7: 92 nodes to be scored (4792 eliminated genes)
##
## Level 6: 82 nodes to be scored (8771 eliminated genes)
##
## Level 5: 64 nodes to be scored (10641 eliminated genes)
##
## Level 4: 40 nodes to be scored (12777 eliminated genes)
##
## Level 3: 38 nodes to be scored (14124 eliminated genes)
##
## Level 2: 2 nodes to be scored (14553 eliminated genes)
##
## Level 1: 1 nodes to be scored (14702 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/CCDown'), recursive=TRUE)
#GOAnnotation(ResCCDown$ResSel, GOdata=ResCCDown$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/CCDown'), keytype='SYMBOL')GOTable(ResCCDownHT$ResSel, maxGO=20)# Wrapper function for topGO analysis (see helper file)
ResCCUpHT <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=CCannHT, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCUpHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1707 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3236 relations. )
##
## Annotating nodes ...............
## ( 14776 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 591 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 26 nodes to be scored (35 eliminated genes)
##
## Level 10: 53 nodes to be scored (55 eliminated genes)
##
## Level 9: 84 nodes to be scored (774 eliminated genes)
##
## Level 8: 87 nodes to be scored (2513 eliminated genes)
##
## Level 7: 100 nodes to be scored (4980 eliminated genes)
##
## Level 6: 90 nodes to be scored (8744 eliminated genes)
##
## Level 5: 64 nodes to be scored (10657 eliminated genes)
##
## Level 4: 39 nodes to be scored (12791 eliminated genes)
##
## Level 3: 41 nodes to be scored (14126 eliminated genes)
##
## Level 2: 2 nodes to be scored (14552 eliminated genes)
##
## Level 1: 1 nodes to be scored (14702 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/CCUp'), recursive=TRUE)
#GOAnnotation(ResCCUp$ResSel, GOdata=ResCCUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/CCUp'), keytype='SYMBOL')GOTable(ResCCUpHT$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResCCAllHT$ResSel, TopGOResDown=ResCCDownHT$ResSel, TopGOResUp=ResCCUpHT$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResCCAllHT$ResSel, TopGOResDown = ResCCDownHT$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`Most of the useful information has been saved during the analysis. Here I save figures, workspace and information about the session.
if (params$SaveImages == TRUE){ #Just in case since eval only works when knitting
#Set the folder paths
from <- paste(getwd(), paste(params$NbName, 'files/figure-html', sep='_'), sep='/')
to <- params$FiguresFolder
#Copy to output directory
file.copy(from, to, recursive = TRUE, copy.mode = TRUE)
}ResSelBP_HT <- list(ResSelAll = ResBPAllHT$ResSel,
ResSelUp = ResBPUpHT$ResSel,
ResSelDown = ResBPDownHT$ResSel)
ResSelMF_HT <- list(ResSelAll = ResMFAllHT$ResSel,
ResSelUp = ResMFUpHT$ResSel,
ResSelDown = ResMFDownHT$ResSel)
ResSelCC_HT <- list(ResSelAll = ResCCAllHT$ResSel,
ResSelUp = ResCCUpHT$ResSel,
ResSelDown = ResCCDownHT$ResSel)
ResSel_HT <- list(BP = ResSelBP_HT,
MF = ResSelMF_HT,
CC = ResSelCC_HT)
saveRDS(ResSel_HT, paste0(SavingFolder, '/day25CbO.', 'ResSel_HT.rds'))SessionInfo <- sessionInfo()
Date <- date()
save.image(paste0(SavingFolder, '/day25CbO.', 'FunctionalAnalysisWorkspace_HT.RData'))